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research-article

Multimedia News Summarization in Search

Published: 01 February 2016 Publication History

Abstract

It is a necessary but challenging task to relieve users from the proliferative news information and allow them to quickly and comprehensively master the information of the whats and hows that are happening in the world every day. In this article, we develop a novel approach of multimedia news summarization for searching results on the Internet, which uncovers the underlying topics among query-related news information and threads the news events within each topic to generate a query-related brief overview. First, the hierarchical latent Dirichlet allocation (hLDA) model is introduced to discover the hierarchical topic structure from query-related news documents, and a new approach based on the weighted aggregation and max pooling is proposed to identify one representative news article for each topic. One representative image is also selected to visualize each topic as a complement to the text information. Given the representative documents selected for each topic, a time-bias maximum spanning tree (MST) algorithm is proposed to thread them into a coherent and compact summary of their parent topic. Finally, we design a friendly interface to present users with the hierarchical summarization of their required news information. Extensive experiments conducted on a large-scale news dataset collected from multiple news Web sites demonstrate the encouraging performance of the proposed solution for news summarization in news retrieval.

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    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 7, Issue 3
    Regular Papers, Survey Papers and Special Issue on Recommender System Benchmarks
    April 2016
    472 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/2885506
    • Editor:
    • Yu Zheng
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 01 February 2016
    Accepted: 01 September 2015
    Revised: 01 July 2015
    Received: 01 October 2014
    Published in TIST Volume 7, Issue 3

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    Author Tags

    1. News summarization
    2. hierarchical latent Dirichlet allocation
    3. maximum spanning tree
    4. multimodal
    5. topic structure

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    • (2023)Supporting Video Authoring for Communication of Research ResultsProceedings of the 2023 ACM International Conference on Interactive Media Experiences10.1145/3573381.3596157(47-59)Online publication date: 12-Jun-2023
    • (2023)Temporal Dynamic Concept Modeling Network for Explainable Video Event RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/356831219:6(1-22)Online publication date: 12-Jul-2023
    • (2023)Look&listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech EnhancementIEEE Transactions on Multimedia10.1109/TMM.2022.319910925(5800-5812)Online publication date: 2023
    • (2023)Multimodal-Based and Aesthetic-Guided Narrative Video SummarizationIEEE Transactions on Multimedia10.1109/TMM.2022.318339425(4894-4908)Online publication date: 2023
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    • (2023)FakeIDCA: Fake news detection with incremental deep learning based concept drift adaptionMultimedia Tools and Applications10.1007/s11042-023-16588-z83:10(28579-28594)Online publication date: 6-Sep-2023
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